发表机构
Technical University of Munich; Munich Institute of Robotics and Machine Intelligence (MIRMI)（慕尼黑工业大学; 慕尼黑机器人与机器智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自主赛车轨迹规划保守导致性能损失的问题，提出控制感知在线轨迹规划框架，通过动态调整约束补偿执行误差，在不增加计算负担的前提下缩短单圈时间1.8秒。
AI 中文摘要
自主赛车在车辆动力学极限下运行，微小的控制误差会转化为危及安全的行为并导致性能损失。轨迹规划器假设跟踪完美，对执行误差视而不见；为保证安全，它们因此限制自身采用保守的空间边界，导致可用赛道空间未被利用。为克服这些问题，我们提出一种控制感知的在线轨迹规划框架，该框架从自身执行误差中学习：通过运行时测量系统性跟踪偏差，动态调整赛道空间约束并迭代扩展自由空间规划区域，规划器在保持时间最优的同时补偿累积的执行误差。该方法在搭载自主赛车的高保真闭环仿真环境中进行分析，结果表明，我们的方法将单圈时间缩短1.8秒，且未增加计算负担，保持了25毫秒的中位数运行时间。我们的发现表明，将控制诱导的偏差反馈到规划层，可解锁模块化架构此前无法实现的性能，使自主车辆能够系统地利用赛道极限。
英文摘要
Autonomous racecars operate at the limits of vehicle dynamics, where small control errors translate into safety-critical behavior and lost performance. Trajectory planners assume perfect tracking and remain blind to execution errors. To guarantee safety, trajectory planners therefore restrict themselves to conservative spatial margins, leaving usable track space untapped. To overcome these issues, we introduce a control-informed online trajectory planning framework that learns from its own execution errors. By measuring systematic tracking deviations during runtime, we dynamically adapt spatial track constraints and iteratively expand the free-space planning area. The planner remains time-optimal while compensating for accumulated execution errors. This method was analyzed in a high-fidelity closed-loop simulation environment with autonomous racecars. The results demonstrate that our approach reduces lap time by 1.8\,s without increasing computational burden, maintaining a median runtime of 25 ms. Our finding indicates that feeding control-induced deviations back into the planning layer unlocks performance previously inaccessible to modular architectures and enables autonomous vehicles to exploit track limits systematically.
CommentsAccepted at IEEE ITSC 2026
